Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for initializing a campaign control system comprising: receiving, from a campaign control system, a request for price/volume relationship information, the request including an identifier of a target audience; determining a plurality of prices associated with one or more volumes based on previously collected price/volume data of an event for the target audience; defining a plurality of configuration parameters, wherein the plurality of configuration parameters comprises a first configuration parameter representative of a minimum difference in price between adjacent members of a representation of a price/volume curve, a second configuration parameter representative of a maximum difference in volume between adjacent members of the representation of the price/volume curve, and a third configuration parameter representative of a minimum difference in volume between adjacent members of the representation of the price/volume curve; responsive to the request for price/volume relationship information from the campaign control system, generating a representation of the price/volume curve based on the plurality of prices associated with the one or more volumes and the plurality of configuration parameters, wherein the generating comprises determining a first price within the representation of the price/volume curve based on a determination that a difference between the first price and a second price adjacent to the first price meets the minimum difference in price represented by the first configuration parameter, determining a third price within the representation of the price/volume curve based on a determination that a difference between a first volume associated with the third price and a second volume associated with a fourth price adjacent to the third price does not exceed the maximum difference in volume represented by the second configuration parameter, and determining a fifth price within the representation of the price/volume curve based on a determination that a difference between a third volume associated with the fifth price and a fourth volume associated with a sixth price adjacent to the fifth price meets the minimum difference in volume represented by the third configuration parameter; and outputting the representation of the price/volume curve, comprising the first price, the third price and the fifth price, to the campaign control system to enable the campaign control system to determine an initial bid calculated, utilizing the representation of the price/volume curve, to achieve a desired pacing.
This invention relates to a computer-implemented method for initializing a campaign control system by generating a price/volume curve to optimize bidding strategies. The method addresses the challenge of dynamically adjusting bids in advertising campaigns to achieve desired pacing, ensuring efficient allocation of advertising spend over time. The system receives a request from a campaign control system, including an identifier for a target audience, and retrieves previously collected price/volume data for an event associated with that audience. Using this data, the system determines multiple prices corresponding to different volumes. Configuration parameters are defined to control the representation of the price/volume curve, including a minimum price difference between adjacent points, a maximum volume difference between adjacent points, and a minimum volume difference between adjacent points. The system then generates a price/volume curve by selecting prices and volumes that meet these constraints. For example, a first price is chosen if its difference from an adjacent price meets the minimum price threshold, a third price is selected if the volume difference from an adjacent price does not exceed the maximum volume threshold, and a fifth price is included if the volume difference from another adjacent price meets the minimum volume threshold. The resulting curve, comprising these prices, is output to the campaign control system, enabling it to calculate an initial bid that aligns with the desired pacing for the campaign. This approach ensures that bidding strategies are data-driven and adaptable to real-time conditions.
2. The computer-implemented method of claim 1 , comprising: defining at least one configuration parameter of the plurality of configuration parameters based on user input.
This invention relates to a computer-implemented method for managing configuration parameters in a system. The method addresses the challenge of efficiently setting and adjusting multiple configuration parameters, which is often complex and time-consuming, especially in large or dynamic systems where parameters may need frequent updates. The method involves defining at least one configuration parameter from a set of parameters based on user input. This allows users to customize or modify system settings dynamically, ensuring the system operates according to specific requirements. The method may also include generating a configuration file that stores these parameters, enabling the system to load and apply them during operation. Additionally, the method may validate the parameters to ensure they meet predefined criteria, such as data type, range, or format, before applying them. This validation step helps prevent errors and ensures system stability. The method may further include monitoring the system's performance after applying the configuration parameters, allowing for real-time adjustments if needed. This feedback loop helps maintain optimal system performance. The method may also support importing or exporting configuration parameters, making it easier to transfer settings between different systems or environments. Overall, the invention provides a flexible and user-friendly approach to managing configuration parameters in a computer system.
3. The computer-implemented method of claim 1 , comprising: dynamically learning at least one configuration parameter of the plurality of configuration parameters.
This invention relates to a computer-implemented method for dynamically adjusting configuration parameters in a system to optimize performance. The method addresses the problem of static or manually configured systems that fail to adapt to changing conditions, leading to inefficiencies or suboptimal performance. The system monitors operational metrics and dynamically adjusts configuration parameters based on real-time data to improve efficiency, reliability, or other performance metrics. The method involves collecting performance data from the system, analyzing the data to identify trends or deviations, and automatically adjusting configuration parameters in response. The adjustments are made without manual intervention, allowing the system to self-optimize. The method may also include learning from historical data to refine future adjustments, ensuring continuous improvement over time. The dynamic learning process may involve machine learning techniques, statistical analysis, or rule-based logic to determine the most effective parameter values. The system may apply these adjustments across multiple components or subsystems, ensuring cohesive optimization. The method can be used in various domains, including cloud computing, network management, or industrial automation, where adaptive configuration is critical for maintaining performance under varying conditions. By dynamically learning and adjusting parameters, the system avoids the limitations of static configurations, leading to more responsive and efficient operation.
4. The computer-implemented method of claim 1 , comprising: dynamically learning, via machine learning algorithms, at least one configuration parameter of the plurality of configuration parameters.
This invention relates to a computer-implemented method for optimizing system performance by dynamically adjusting configuration parameters using machine learning. The method addresses the challenge of manually tuning system parameters, which is time-consuming and often ineffective due to the complexity of modern systems. By leveraging machine learning, the method automatically learns and updates configuration parameters to improve performance, efficiency, or other desired outcomes. The method involves collecting performance data from a system, such as response times, resource utilization, or error rates. Machine learning algorithms analyze this data to identify optimal or near-optimal configurations. The learned parameters are then applied to the system, and the process repeats iteratively to adapt to changing conditions. The machine learning models may use techniques such as reinforcement learning, supervised learning, or unsupervised learning, depending on the available data and objectives. The invention also includes preprocessing steps to prepare the data for machine learning, such as normalization or feature extraction. Additionally, it may incorporate validation techniques to ensure the learned parameters do not degrade system stability or security. The method can be applied to various systems, including software applications, cloud services, or hardware configurations, where dynamic optimization is beneficial. By automating parameter tuning, the invention reduces manual effort and improves system adaptability.
5. The computer-implemented method of claim 1 , wherein at least one configuration parameter of the plurality of configuration parameters is pre-defined.
This invention relates to a computer-implemented method for managing configuration parameters in a system. The method addresses the problem of efficiently handling configuration parameters, which are settings that control the behavior of software or hardware components. The challenge is to ensure that these parameters are properly defined, validated, and applied without causing system errors or performance issues. The method involves a plurality of configuration parameters that are used to configure a system. At least one of these parameters is pre-defined, meaning it is set in advance before the system is deployed or during runtime. Pre-defining parameters helps standardize configurations, reduce manual errors, and ensure consistency across different deployments. The method may also include steps to validate these parameters, apply them to the system, and monitor their impact on system performance. The invention may further include mechanisms to dynamically adjust pre-defined parameters based on system conditions or user inputs. This flexibility allows the system to adapt to changing requirements while maintaining stability. The method ensures that configuration changes are applied in a controlled manner, minimizing disruptions to system operations. By pre-defining at least one configuration parameter, the method simplifies the configuration process, reduces the risk of misconfigurations, and improves system reliability. This approach is particularly useful in large-scale systems where manual configuration management is impractical or error-prone.
6. The computer-implemented method of claim 1 , wherein at least one configuration parameter of the plurality of configuration parameters is programmatically defined.
This invention relates to computer-implemented methods for managing configuration parameters in software systems. The problem addressed is the need for dynamic and automated configuration management, particularly in complex systems where manual configuration is inefficient or impractical. The method involves programmatically defining at least one configuration parameter among a plurality of configuration parameters. This allows the system to automatically adjust settings based on predefined rules, environmental conditions, or runtime data, rather than requiring manual intervention. The programmable definition of parameters enables greater flexibility, scalability, and adaptability in software systems, reducing the risk of human error and improving system performance. The method may also include dynamically updating configuration parameters in response to changes in system state, user input, or external factors. This ensures that the system remains optimized and responsive to varying conditions. Additionally, the method may involve validating configuration parameters to ensure they meet specified criteria before applying them, preventing invalid or harmful settings from being used. By programmatically defining configuration parameters, the invention provides a more robust and efficient way to manage system configurations, particularly in large-scale or distributed environments where manual configuration is impractical. This approach enhances system reliability, reduces maintenance overhead, and improves overall performance.
7. The computer-implemented method of claim 1 , wherein the event is an impression, click, conversion, or a view.
This invention relates to digital advertising analytics, specifically tracking and analyzing user interactions with online advertisements. The problem addressed is the need for accurate and detailed measurement of user engagement with ads, which is critical for advertisers to assess campaign effectiveness and optimize spending. The method involves monitoring and recording various types of user interactions with digital advertisements. These interactions include impressions (when an ad is displayed to a user), clicks (when a user interacts with the ad), conversions (when a user completes a desired action after seeing the ad, such as making a purchase), and views (when a user actively watches or engages with the ad content). The system captures these events in real-time, allowing advertisers to analyze performance metrics and adjust strategies accordingly. The data collected can be used to generate reports, optimize ad placements, and improve targeting to maximize return on investment. The method ensures comprehensive tracking of user behavior, providing a holistic view of ad performance across different engagement levels. This helps advertisers make data-driven decisions to enhance campaign efficiency and effectiveness.
8. The computer-implemented method of claim 1 , wherein the representation of the price/volume curve is a vector.
A system and method for analyzing financial market data involves generating a vector-based representation of a price/volume curve to improve predictive modeling. The price/volume curve represents the relationship between trading prices and corresponding trading volumes over time. By converting this curve into a vector, the system enables efficient processing and analysis, particularly for machine learning models. The vector representation captures key characteristics of the curve, such as trends, volatility, and liquidity patterns, in a structured format that can be easily integrated into predictive algorithms. This approach enhances the accuracy of financial forecasts by providing a standardized input for models that analyze market behavior. The method is particularly useful in high-frequency trading, where rapid and precise data processing is critical. The vector-based representation allows for faster computations and better generalization across different market conditions. Additionally, the system may include preprocessing steps to normalize or smooth the price/volume data before vectorization, ensuring consistency and reliability in the analysis. The overall solution addresses the challenge of efficiently extracting meaningful insights from complex financial datasets, enabling more informed trading decisions.
9. One or more computer-readable storage media having instructions embodied thereon which, when executed by one or more processors, cause the one or more processors to: receive a request for price/volume information, the request including an identifier of a target audience; determine a plurality of prices associated with one or more volumes based on previously collected price/volume data of an event for the target audience; responsive to the request for price/volume relationship information, generate a vector representation of a price/volume curve based on the plurality of prices associated with the one or more volumes, wherein the generating comprises at least one of determining a first price within the vector representation of the price/volume curve based on a determination that a difference between the first price and a second price adjacent to the first price meets a minimum difference in price represented by a first configuration parameter, determining a third price within the vector representation of the price/volume curve based on a determination that a difference between a first volume associated with the third price and a second volume associated with a fourth price adjacent to the third price does not exceed a maximum difference in volume represented by a second configuration parameter, or determining a fifth price within the vector representation of the price/volume curve based on a determination that a difference between a third volume associated with the fifth price and a fourth volume associated with a sixth price adjacent to the fifth price meets a minimum difference in volume represented by a third configuration parameter; and output the vector representation of the price/volume curve to a dashboard to aid a user of the dashboard in determining aspects of an online marketing campaign that includes the target audience.
This invention relates to a system for generating and displaying price/volume relationship data to optimize online marketing campaigns. The system addresses the challenge of dynamically analyzing pricing strategies for events based on audience-specific demand patterns. When a request for price/volume information is received, the system processes previously collected data to determine multiple price points associated with different sales volumes for a target audience. It then generates a vector representation of a price/volume curve, which is a graphical or mathematical model showing how price changes affect sales volume. The curve is constructed by applying configurable parameters to ensure meaningful price and volume differences between adjacent data points. For example, the system may include a price point if the difference between it and a neighboring price meets a minimum threshold, or exclude a volume if the difference between it and an adjacent volume exceeds a maximum threshold. The resulting vector representation is displayed on a dashboard, providing users with actionable insights to adjust pricing strategies for online marketing campaigns targeting specific audiences. This helps marketers optimize revenue and demand forecasting.
10. The one or more computer-readable storage media of claim 9 , wherein the instructions cause the one or more processors to: partition the previously collected price/volume data, wherein to generate the vector representation of the price/volume curve causes the one or more processors to: create the vector representation of the price/volume curve from a price value and volume value for each partition that results from the partitioning.
This invention relates to financial data analysis, specifically the processing of price and volume data to generate vector representations of price/volume curves. The problem addressed is the efficient and accurate transformation of raw financial data into structured formats that can be used for further analysis, such as predictive modeling or trading strategy development. The system involves partitioning previously collected price/volume data into segments. For each partition, a vector representation of the price/volume curve is generated by extracting key values, including a price value and a volume value. This vector representation simplifies the data while preserving essential characteristics, making it easier to analyze trends, patterns, or anomalies in financial markets. The partitioning step ensures that the data is processed in manageable segments, which can improve computational efficiency and scalability. The method allows for the decomposition of complex financial data into structured vectors, which can then be used in machine learning models, statistical analysis, or algorithmic trading systems. By focusing on price and volume values within each partition, the system captures critical market dynamics while reducing noise and irrelevant information. This approach enhances the accuracy and reliability of financial data analysis, supporting better decision-making in trading and investment strategies.
11. The one or more computer-readable storage media of claim 9 , wherein the instructions cause the one or more processors to: partition the previously collected price/volume data, wherein partitioning the previously collected price/volume data is based on a minimum desired price difference between adjacent prices.
This invention relates to financial data analysis, specifically methods for processing and partitioning price/volume data to improve trading strategies or market analysis. The problem addressed is the need to efficiently organize and analyze large datasets of price and volume information to identify meaningful patterns or trading opportunities. The invention involves a computer-implemented system that processes previously collected price/volume data by partitioning it based on a minimum desired price difference between adjacent prices. This partitioning ensures that adjacent prices in the dataset are separated by at least a specified threshold, which helps in reducing noise and highlighting significant price movements. The system may also include preprocessing steps such as filtering or normalizing the data before partitioning. The partitioned data can then be used for further analysis, such as identifying trends, volatility patterns, or trading signals. The partitioning process involves comparing adjacent price points in the dataset and adjusting the data structure to enforce the minimum price difference. This ensures that the resulting dataset is more structured and easier to analyze, particularly for applications like algorithmic trading, risk assessment, or market forecasting. The invention may be implemented as part of a larger financial analysis platform or as a standalone tool for traders and analysts.
12. The one or more computer-readable storage media of claim 9 , wherein the instructions cause the one or more processors to: partition the previously collected price/volume data, wherein partitioning the previously collected price/volume data based on a volume difference of an event between adjacent partitions includes partitioning the previously collected price/volume data based on a maximum relative difference in volume between the adjacent partitions.
This invention relates to financial data analysis, specifically partitioning price/volume data to improve market event detection. The problem addressed is the need to accurately segment financial market data to identify significant events, such as price movements or trading volume spikes, which are often obscured by noise or irrelevant fluctuations. The solution involves partitioning historical price/volume data into segments where the volume difference between adjacent partitions is maximized relative to their respective volumes. This partitioning method ensures that each segment represents a distinct market event, enhancing the ability to analyze and interpret market behavior. The approach dynamically adjusts the segmentation based on volume changes, ensuring that partitions reflect meaningful shifts in trading activity rather than arbitrary time intervals. By focusing on relative volume differences, the method improves the detection of significant market events while reducing the impact of minor fluctuations. This technique is particularly useful for algorithmic trading, risk assessment, and market surveillance, where accurate event identification is critical for decision-making. The partitioning process is automated and applied to previously collected price/volume data, enabling real-time or historical analysis of market dynamics.
13. The one or more computer-readable storage media of claim 12 , wherein the maximum relative difference in volume is represented as a maximum percentage change in volume between the adjacent partitions.
The invention relates to data storage systems, specifically methods for managing storage partitions to optimize performance and reliability. The problem addressed is ensuring consistent performance across storage partitions by controlling the relative differences in their volumes. This is particularly important in distributed storage systems where uneven partition sizes can lead to imbalances in data distribution, affecting read/write operations and system efficiency. The invention involves a method for determining and enforcing a maximum relative difference in volume between adjacent storage partitions. This difference is represented as a maximum percentage change in volume, ensuring that no partition deviates beyond a predefined threshold from its neighboring partitions. By maintaining this constraint, the system avoids performance bottlenecks caused by disproportionately large or small partitions, leading to more balanced data distribution and improved system efficiency. The method includes steps for calculating the volume of each partition, comparing these volumes to adjacent partitions, and adjusting partition sizes if the relative difference exceeds the specified maximum percentage. This ensures that all partitions remain within an acceptable range of each other, preventing uneven workload distribution and maintaining consistent performance across the storage system. The approach is particularly useful in large-scale storage environments where maintaining balance is critical for optimal operation.
14. The one or more computer-readable storage media of claim 9 , wherein the instructions cause the one or more processors to: partition the previously collected price/volume data, wherein partitioning the previously collected price/volume data based on a volume difference of an event between adjacent partitions includes partitioning the previously collected price/volume data based on a minimum desired difference in volume between the adjacent partitions.
This invention relates to financial data analysis, specifically partitioning price/volume data to improve market event detection. The problem addressed is the need to accurately segment financial data to identify significant market events based on volume differences. The invention provides a method for partitioning previously collected price/volume data by analyzing volume differences between adjacent partitions. The partitioning process ensures that the volume difference between adjacent partitions meets a minimum desired threshold, which helps in isolating distinct market events for further analysis. This approach enhances the detection of meaningful price/volume patterns by reducing noise and improving the granularity of event segmentation. The method is implemented using computer-readable storage media containing instructions that, when executed, perform the partitioning based on the specified volume difference criteria. The system may also include additional features such as collecting and storing price/volume data, analyzing the partitioned data to identify market events, and generating alerts or reports based on the detected events. The invention is particularly useful in algorithmic trading, risk management, and financial forecasting, where precise event detection is critical for decision-making.
15. A system, comprising: one or more processors; and memory, coupled with the one or more processors, having instructions stored thereon, which, when executed by the one or more processors cause the one or more processors to: receive a request for price/volume information for a target event, the request including an identifier of a target audience; partition previously collected price/volume data into a plurality of partitions based on a magnitude of difference in volume between adjacent partitions; define a plurality of configuration parameters, wherein the plurality of configuration parameters comprises at least one of a first configuration parameter representative of a minimum difference in price between adjacent members of a vector representation of a price/volume curve, a second configuration parameter representative of a maximum difference in volume between adjacent members of the vector representation of the price/volume curve, or a third configuration parameter representative of a minimum difference in volume between adjacent members of the vector representation of the price/volume curve; create the vector representation of the price/volume curve that includes a price value and volume value for each of the plurality of partitions based on the plurality of configuration parameters, wherein the creating comprises at least one of determining a first price within the vector representation of the price/volume curve based on a determination that a difference between the first price and a second price adjacent to the first price meets the minimum difference in price represented by the first configuration parameter, determining a third price within the vector representation of the price/volume curve based on a determination that a difference between a first volume associated with the third price and a second volume associated with a fourth price adjacent to the third price does not exceed the maximum difference in volume represented by the second configuration parameter, or determining a fifth price within the vector representation of the price/volume curve based on a determination that a difference between a third volume associated with the fifth price and a fourth volume associated with a sixth price adjacent to the fifth price meets the minimum difference in volume represented by the third configuration parameter; and output the vector representation of the price/volume curve to one of: a dashboard to aid a user of the dashboard in determining aspects of an online marketing campaign that includes the target audience; or a campaign control system to enable the campaign control system to determine an initial bid calculated, utilizing the vector representation of the price/volume curve, to achieve a desired pacing.
The system provides a method for analyzing and utilizing price/volume data to optimize online marketing campaigns. The system processes historical price/volume data to generate a vector representation of a price/volume curve, which helps in determining optimal bidding strategies or campaign adjustments. When a request for price/volume information is received, the system partitions the existing data into multiple segments based on volume differences between adjacent partitions. Configuration parameters are defined to control the granularity of the vector representation, including minimum price differences, maximum volume differences, and minimum volume differences between adjacent points in the curve. The system then constructs the vector representation by ensuring that adjacent price points meet the specified minimum price difference, adjacent volume points do not exceed the maximum volume difference, and volume differences between certain price points meet the minimum volume threshold. The resulting vector representation is output to either a dashboard for user analysis or a campaign control system to calculate initial bids that achieve desired pacing for the marketing campaign. This approach enables more precise and data-driven decision-making in online advertising.
16. The system of claim 15 , wherein to partition the previously collected price/volume data into a plurality of partitions based on a magnitude of difference in volume between adjacent partitions is based on a minimum desired price difference between adjacent prices.
The system relates to financial data analysis, specifically partitioning price/volume data to improve market trend analysis. The problem addressed is the difficulty in identifying meaningful patterns in financial data due to noise and irregularities in price and volume fluctuations. The system partitions previously collected price/volume data into multiple segments based on volume differences between adjacent partitions, ensuring that adjacent partitions have a minimum desired price difference. This partitioning helps in isolating distinct market behaviors, reducing noise, and enhancing the accuracy of trend detection. The partitioning process involves analyzing volume changes and adjusting the segments to maintain the specified price difference threshold. This approach allows for more precise identification of market movements, improving decision-making for traders and analysts. The system may also include additional features such as real-time data processing, historical data comparison, and predictive modeling to further refine market insights. The partitioning method ensures that the data is segmented in a way that highlights significant price changes while maintaining consistency in volume-based segmentation. This technique is particularly useful in high-frequency trading and algorithmic trading strategies where rapid and accurate data interpretation is critical.
17. The system of claim 15 , wherein the magnitude of difference in volume between adjacent partitions includes a maximum relative difference in volume between the adjacent partitions.
This invention relates to a system for managing fluid distribution in a partitioned container, addressing the challenge of ensuring uniform fluid allocation across multiple compartments. The system includes a container divided into multiple partitions, each with a defined volume, and a mechanism to control fluid flow between these partitions. The key innovation involves regulating the volume difference between adjacent partitions to maintain a maximum relative difference in volume, ensuring balanced fluid distribution. This prevents overfilling or underfilling of any single partition, which could lead to inefficiencies or operational failures. The system may incorporate sensors to monitor fluid levels and actuators to adjust partition volumes dynamically. The maximum relative difference in volume between adjacent partitions is a critical parameter, ensuring that the system operates within predefined limits to maintain stability and performance. This approach is particularly useful in applications requiring precise fluid management, such as chemical processing, pharmaceutical manufacturing, or industrial storage systems. The system may also include feedback mechanisms to continuously adjust partition volumes based on real-time data, enhancing accuracy and reliability. By enforcing a controlled volume difference, the invention optimizes fluid distribution while minimizing waste and operational disruptions.
18. The system of claim 17 , wherein the maximum relative difference in volume is represented as a maximum percentage change in volume between the adjacent partitions.
The system relates to a method for analyzing and comparing the volumes of adjacent partitions within a three-dimensional space, such as a geological formation or a fluid storage container. The problem addressed is the need to accurately quantify and monitor changes in volume between adjacent partitions to detect anomalies, optimize resource distribution, or ensure structural integrity. The system includes a partitioning module that divides the three-dimensional space into discrete partitions, each with a defined volume. A volume measurement module calculates the volume of each partition, and a comparison module determines the relative difference in volume between adjacent partitions. The system further includes a thresholding module that evaluates whether the relative difference exceeds a predefined maximum percentage change, indicating a significant deviation. This allows for real-time monitoring and alerts when volume discrepancies surpass acceptable limits. The system may also include a visualization module to display the partitions and their volume differences graphically, aiding in interpretation and decision-making. The invention is particularly useful in applications such as reservoir management, structural health monitoring, or fluid dynamics analysis, where precise volume tracking is critical.
19. The system of claim 15 , wherein the magnitude of difference in volume between adjacent partitions includes a minimum desired difference in volume between the adjacent partitions.
This invention relates to a system for managing fluid distribution in a partitioned container, addressing the challenge of ensuring consistent and controlled fluid allocation across multiple compartments. The system includes a container divided into multiple partitions, each with adjustable volume capacity, and a control mechanism that regulates fluid flow between partitions. The control mechanism monitors and adjusts the volume of each partition to maintain a specified difference in volume between adjacent partitions, ensuring optimal fluid distribution. The system may also include sensors to detect fluid levels and actuators to modify partition sizes dynamically. The invention further specifies that the volume difference between adjacent partitions must meet a minimum desired threshold, preventing excessive imbalance and ensuring efficient fluid management. This approach is particularly useful in applications requiring precise fluid allocation, such as chemical processing, medical devices, or industrial storage systems. The system dynamically adjusts partition volumes to maintain the minimum difference, enhancing operational stability and performance.
20. The system of claim 15 , wherein to partition the previously collected price/volume data into a plurality of partitions is based on a maximum relative difference in volume between the adjacent partitions, a minimum desired difference in volume between the adjacent partitions, and a minimum desired price difference between the adjacent partitions.
The system is designed for analyzing financial market data, specifically price and volume information, to improve trading strategies or market predictions. The core challenge addressed is efficiently partitioning historical price/volume data into meaningful segments for analysis, ensuring that each partition maintains statistically significant differences in both volume and price relative to adjacent partitions. This partitioning helps identify trends, anomalies, or trading opportunities that may be obscured in raw, unsegmented data. The system partitions the data based on three key criteria: a maximum relative difference in volume between adjacent partitions, a minimum desired difference in volume between adjacent partitions, and a minimum desired price difference between adjacent partitions. The maximum relative difference ensures that adjacent partitions do not overlap excessively in volume, preventing redundant or overlapping analysis. The minimum desired differences in volume and price enforce that partitions are distinct enough to be meaningful, avoiding trivial or insignificant segments. This structured partitioning allows for more accurate pattern recognition, risk assessment, and decision-making in financial markets. The method ensures that the data is divided in a way that preserves critical market dynamics while enabling efficient computational analysis.
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January 12, 2021
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